Accounting for missing data caused by drug cessation in observational comparative effectiveness research: a simulation study
Year: 2022
Type: article
Abstract: OBJECTIVES: To assess the performance of statistical methods used to compare the effectiveness between drugs in an observational setting in the presence of attrition. METHODS: In this simulation study, we compared the estimations of low disease activity (LDA) at 1 year produced by complete case analysis (CC), last observation carried forward (LOCF), LUNDEX, non-responder imputation (NRI), inverse probability weighting (IPW) and multiple imputations of the outcome. All methods were adjusted for confounders. The reasons to stop the treatments were included in the multiple imputation method (confounder-adjusted response rate with attrition correction, CARRAC) and were either included (IPW2) or not (IPW1) in the IPW method. A realistic simulation data set was generated from a real-world data collection. The amount of missing data caused by attrition and its dependence on the 'true' value of the data missing were varied to assess the robustness of each method to these changes. RESULTS: LUNDEX and NRI strongly underestimated the absolute LDA difference between two treatments, and their estimates were highly sensitive to the amount of attrition. IPW1 and CC overestimated the absolute LDA difference between the two treatments and the overestimation increased with increasing attrition or when missingness depended on disease activity at 1 year. IPW2 and CARRAC produced unbiased estimations, but IPW2 had a greater sensitivity to the missing pattern of data and the amount of attrition than CARRAC. CONCLUSIONS: Only multiple imputation and IPW2, which considered both confounding and treatment cessation reasons, produced accurate comparative effectiveness estimates.
Source: Annals of the Rheumatic Diseases
Authors: Denis Mongin, Kim Lauper, Axel Finckh, Thomas Frisell, Delphine Sophie Courvoisier
Institutions: University of Geneva, Hôpital Beau-Séjour, Karolinska Institutet
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,870
Funder: AbbVie
OpenAlex ID: W4206409103